Var3PPred

Var3PPred: Variant prediction based on 3-D structure and sequence analyses of protein-protein interactions on Autoinflammatory Disease

Var3PPred is a pathogenicity classifier designed to identify pathogenic variants in genes associated with autoinflammatory disorders. This comprehensive approach integrates protein-protein interaction analysis and 3D structural information.

Dataset

• Source: Infevers database
• Variants: 702 missense disease-associated variants from 35 genes linked to Systemic Autoinflammatory Diseases (SAIDs)
• Balancing: SMOTE algorithm used to balance dataset (130 benign and 572 pathogenic variations)

Methodology

• Protein-Protein Interaction Analysis: Utilized STRING and Intact databases for 3D docking analysis
• Feature Integration: Integrated sequential and structural features including ∆∆G, accessible surface area, volume, pLDDT scores, and PSIC scores
• Feature Calculation: Features calculated using PyRosetta and AF2 computed structures

Machine Learning

• Algorithms: Six algorithms tested, with random forest classifier found to be most effective
• Performance: Random forest achieved AUROC of 99% on test set, outperforming SIFT, PolyPhen, and CADD on an unseen test set of a SAID-related gene

The Merge-Last-vus.csv file contains descriptions of Variants of Unknown Significance (VUS) along with their Var3PPred predictions in the last column (Prediction(0-1), 0: Benign, 1: Pathogenic). VEP annotations for these variants are provided in the VEP-Annotation-variants.csv file. The Model_Training.py script contains training methods and SHAP analysis scripts, while the ZDOCK_and_SPRINT_weighted-scores.py script assigns weights to ZDOCK and SPRINT scores based on the HGPEC rank score of genes for systemic autoinflammatory diseases. The pairwised_decision_boundaries_of_models.py file contains scripts for analyzing pairwise relationships of features in the six selected machine learning models with decision boundaries.